
In Correlated Random Walk (CRW) swarms, robots perform a correlated
random walk until they acquire an object, which they then transport to the nest
using phototaxis (i.e., motion in response to light). Robots do not perform task
allocation.
We do not expect high levels of self-organization in CRW swarms, because they
perform random walks and are purely reactive; we expect relatively high levels of
sensor and actuator robustness for the same reason. We expect high levels of
reactivity because robots only have a single mode of operation, regardless
of environmental conditions, and will be substantially affected by changes in
adversity.

In Decaying Pheromone Object (DPO) swarms, robots track seen objects
using exponentially decaying pheromones, and determine the “best” object to
acquire using derived information relevance. Robots do not perform task
allocation.
In spatially constrained and unconstrained environments we expect lower levels of
self-organization and scalability than CRW swarms in constrained spaces; the
competition at shared resources (e.g., foraging targets) due to robot memory will
likely outweigh its benefits, especially as time goes on and the swarm
collectively learns where clusters of objects of interest can
be found. In unconstrained spaces, we expect higher levels of self-organization
and scalability.

In STOCHM swarms, robots stochastically choose to do either (1) the
entire foraging task themselves (i.e., retrieving an object and bringing it to
the nest), or (2) one of two subtasks of bringing an acquired object to a
static intermediate drop site (cache), or picking one up from a cache and
bringing it to the nest. These three tasks form a compound task decomposition
graph (fig:tdgraph-foraging, gold tasks [1]}), in which an
overall task can be decomposed into smaller, simpler tasks exactly one way.
STOCHM swarms will also suffer from congestion near the locations of static
caches, which may outweigh the self-organization possible due to multiple tasks.
We also expect higher levels of scalability and reactivity than DPO swarms in
constrained spaces, due to (1) the presence of caches to help regulate traffic,
and (2) the maintenance of the caches by an outside process independent of swarm
behavior. In unconstrained spaces, we expect lower levels of scalability and
reactivity. The sensor and actuator robustness of STOCHM swarms should be very
similar to DPO swarms, but the population dynamics robustness should be higher,
because some of the information about the world model carried by robots is now
encoded in the environment, and the ability to choose from multiple tasks should
also improve collective re-convergence to an efficient steady-state.

In STOCHX swarms, robots can stochastically choose any of the tasks
of the STOCHM algorithm (i.e. exploit existing caches) to perform, but can
also choose to perform tasks related to dynamically creating new caches as
simulation conditions evolve. Robots perform task allocation, according to the
STOCH-N1 algorithm [1]}, a stochastic choice depending on the
location of the most recently executed task within the graph of tasks available
to each robot. The available tasks form a complex task decomposition graph
(fig:tdgraph-foraging, red+gold tasks), in which an overall task can be
decomposed into smaller, simpler tasks in multiple ways.
The dynamic creation of caches should eliminate the negative effects of
congestion near caches we expect with STOCHM, and result in high levels of
self-organization; we expect greater flexibility and population dynamics for the
same reason. However, raw performance may be lower than other algorithms because
the swarm has to perform additional cognitive work throughout operation regarding
dynamic cache management (e.g., exploration vs. exploitation of the
environment).

<FIGURE>